Human gait, a biometric aimed to recognize individuals by the way they walk, has recently come to play an increasingly important role in different applications such as access control and visual surveillance. Although no two body movements are ever the same, gait is as characteristic of an individual analogous to other biometrics. Psychological, medical, and biomechanical studies support the notion that humans effortlessly recognize people by the way they walk and basic gait patterns are unique to each individual. In contrast to many established biometric modalities such as face, fingerprint or retina, gait can be analyzed from a distance and can be observed without notification to the subject or compliance by the subject. In fact, the considerable attention towards this biometric has been due to its ability to ascertain somebody's identity at a distance while being noninvasive and non-perceivable.
However, human gait analysis and assessment involves challenging issues due to the highly flexible structure and self-occlusion of the human body. These issues mandate using complicated processes for the measurement and analysis of gait in marker-less video sequences. For instance, footwear, physical conditions such as pregnancy, leg or foot injuries, or even drunkenness can change the manner of walking. Like most biometrics, gait will inherently change with age. Therefore, gait can disclose more than identity.
Human gait constitutes an essential metric related to a person's health and well-being. Degradation of a person's walking pattern decreases quality of life for the individual and may result in falls and injuries. In one estimate, 1 out of every 3 elder adults (over the age of 65) falls each year. And these related injuries cost $20 billion per year in the United States. There are different types of physiological and anatomical factors that can adversely affect gait, such as neurological maladies (e.g., Parkinson's disease or multiple sclerosis), degradation of the bones, joints or muscles, lower limb injury or pains and geriatric diseases, such as osteoporosis, which affect a large percentage of the population. The common symptoms for these cases include moving with slow pace, unstable standing, tilted walking, mini-step walking, altering velocity, length of the stride and cadence. Therefore, passive monitoring of a person's gait and the detection of deviations from normal patterns can support current frailty assessments leading to an improved and earlier detection of many diseases, or provide valuable information for rehabilitation. On the other hand, assessment is important for recuperative efforts.
The traditional scales used to analyze gait parameters in clinical conditions are semi-subjective, carried out by specialists who observe the quality of a patient's gait by making him/her walk. This is sometimes followed by a survey in which the patient is asked to give a subjective evaluation of the quality of his/her gait. The disadvantage of these methods is that they give subjective measurements, particularly concerning accuracy and precision, which have a negative effect on the diagnosis, follow-up and treatment of the pathologies.
Wearable sensors are being developed to add objectivity and move the assessment into a passive (e.g., home) setting, rather than costly, infrequent clinical assessments. The various wearable sensor-based systems that have been proposed use sensors located on several parts of the body, such as feet, knees, thighs or waist. Different types of sensors are used to capture the various signals to characterize the human gait. However, their major disadvantage is the need to place devices on the subject's body, which may be uncomfortable or intrusive. Also, the use of wearable sensors allows analysis of only a limited number of gait parameters. Besides, the analysis of the signals is computationally complex and presents the problem of excessive noise.
The relationship between stride length and frequency is of fundamental concern to many studies of walking. Other than wearable or ground sensors, cameras are also used to analyze gait. Prior camera-based approaches have included the following:
Marker based: this method requires the subject wear easily detectable markers on the body, usually at joint locations. The 2D or 3D locations of the markers will be extracted in a monocular or multi-camera system. The marker locations or the relationships between them are then used to segment each stride/step.
Marker-less: this category of methods can be divided into two sub-categories: holistic (usually model free) and model based. For holistic methods, human subjects are usually first detected, tracked and segmented; then gait is usually characterized by the statistics of the spatiotemporal patterns generated by the silhouette of the walking person. A set of features/gait signatures is then computed from the patterns for segmentation/recognition, etc. One approach analyzed the auto correlation signals of the image sequence. Another approach used XT & YT slices for gait analysis. Model-based methods apply human body/shape or motion models to recover features of gait mechanics and kinematics. The relationship between body parts will be used to segment each stride/step or for other purposes. Models include generative and discriminative models.
For most gait analysis methods, segmenting gait cycle precisely is one of the most important steps and building blocks. Stride-to-stride measurement of gait signals is essential for disease diagnosing and monitoring, such as Parkinson's. As such diseases usually progress over a long period of time, it is very desirable to enable frequent and objective assessments to continuously understand such patients' ambulatory condition. Gait signals can come from wearable devices or camera data. Current methods for gait analysis include manual or automatic segmentation based on some gait signals such as feet distance or knee angles, etc. Visual inspection of gait from real-time actions or video recordings is subjective and requires a costly trained professional to be present, thereby limiting the frequency at which evaluations can be performed. Wearables capture only a portion of gait signal (depending on where the sensors are positioned) and require compliance of a patient to consistently wear the device if day-to-day measurement is to be taken. Current computer vision techniques can be categorized into marker-based and marker-less approaches. Similar to wearables, marker-based technologies require precise positioning of markers on subjects, which is not feasible for day-to-day monitoring. Monocular marker-less technologies often require identifying human body parts first, which is very challenging due to variations in viewing angle and appearance. Hence, the current monocular marker-less method is usually performed in clinical settings where the viewing angle and camera-to-subject distance are fixed, and the method may not be robust enough in an assisted living or traditional home setting.
It is believed that most vision based methods explore the spatial change of a feature (could be joint location, body part location, or holistic image features, etc.) or the relationship between them (distance between two feet, knee angles, etc.). These methods require accurate detections of body parts or landmarks, which are usually highly view-dependent and very challenging and thus less robust in practice.